The following are the recommendations based on the conclusion drawn: School Administrators should develop

The gap

Evidence profile

Sourced from the recommendations and future work of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 8 journals. Those papers have been cited 68 times in total.

Supporting evidence — 8 representative gaps

• Exploring Teachers’ Lived Experiences in Assessing Authentic Student Learning in AI-influenced Classrooms: A Phenomenological Study (2026) · Journal of Education and Learning Reviews · doi

  In light of the findings of this study, the following recommendations are offered to support teachers, school leaders, and future researchers in responding to the challenges of assessing authentic student learning in AI-influenced classrooms. As teacher-researchers, these suggestions are grounded not only in the data but also in the shared realities of classroom practice. 1. Develop clear school policies on the responsible use of AI Given the increasing presence of artificial intelligence in education, it is essential for schools to establish clear, context-sensitive guidelines on how AI can be appropriately used in academic tasks. These policies should not simply prohibit AI use, but rather define its role as a support tool for learning rather than a substitute for thinking. Clear policies can help reduce confusion among students and teachers, promote academic integrity, and provide a consistent basis for assessment practices. 2. Strengthen authentic and process-based assessment practices Teachers are encouraged to move beyond reliance on polished written outputs and adopt assessment approaches that make student thinking more visible. These may include oral questioning, in-class written tasks, reflective responses, and performance-based assessments. By focusing on how students explain, apply, and engage with concepts, teachers can better ensure that learning is genuine and meaningful, even in AI-rich environments. 3. Integrate science-specific authentic assessment tasks Given the nature of science education, teachers may design tasks that require students to demonstrate understanding in real time and in context. These may include live laboratory demonstrations, oral defense of experimental results, real-time hypothesis formulation, data interpretation activities, and problem-solving tasks conducted under supervised conditions. Such approaches allow teachers to directly observe students’ reasoning processes and reduce overreliance on AI-generated outputs. leaders and educational 4. Provide continuous teacher training on AI and assessment redesign

  generalrecommendations
  Keywords: teachers tasks assessment students authentic learning clear policies support school leaders researchers student teacher given
• Teacher's Readiness On The Integration Of Artificial Intelligence In Teaching: A Basis For An Intervention Plan (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  The following are the recommendations based on the conclusion drawn: School Administrators should develop and implement a comprehensive AI policy that outlines ethical standards, responsible use guidelines, and data privacy protections. The HRDO should conduct continuous professional development, workshops, and certification courses on AI integration to enhance faculty competence particularly for senior faculty members who may require additional technical support. Faculty Members may design assessments that promote critical thinking and minimize excessive student dependency on AI and guide the students in verifying AI-generated content. School Administrators may adopt the intervention plan crafted as a basis in crafting the AI policy in the school Students should be oriented on ethical and responsible AI usage, including proper citation, disclosure of AI assistance, and critical evaluation of AI outputs. Future Researchers may widen the scope of the study since this is limited to faculty members who are teaching in the Tertiary level of and are encouraged to conduct a qualitative part of the study. the University, 1. 2. 3. 4. 5. 6. 2379 Volume 2 Issue 5 (2026)

  generalrecommendations
  Keywords: faculty school members administrators policy ethical responsible conduct critical students following recommendations based conclusion drawn
• Ensuring academic integrity in the age of ChatGPT: Rethinking exam design, assessment strategies, and ethical AI policies in higher education (2024) · Contemporary Educational Technology · cited 67× · doi

  Building on this study’s findings, several avenues for future research and practice are essential to further address the challenges posed by AI technologies in education. First, future studies should look beyond ChatGPT to see how other emerging AI tools and language models affect academic integrity. As new AI technologies with varying capabilities emerge, there is a need for comprehensive research that investigates their specific challenges and opportunities in a variety of educational contexts. Second, empirical research is needed to determine the effectiveness of the proposed exam redesigns and AI detection tools in real-world educational setups. Longitudinal studies that track the effects of innovative assessment strategies like project-based learning, oral exams, and real-time feedback mechanisms will provide helpful information about their impact on student learning, engagement, and academic integrity. Furthermore, field experiments to evaluate the accuracy and adaptability of AI detection software in detecting new types of AI-generated content are critical. Third, future research should investigate the creation and integration of more sophisticated AI detection tools that can keep up with AI models’ rapidly evolving capabilities. To ensure widespread adoption, these tools must be adaptable, detect nuanced AI-generated content, and seamlessly integrate with existing LMS. Fourth, the role of ethical frameworks in guiding the responsible use of AI in education demands more attention. Researchers should explore how HEIs can develop comprehensive, evidence-based policies that prevent AI misuse and promote AI technologies’ ethical and constructive use. This includes investigating best practices for training educators and students in AI literacy and responsible use. Finally, future research should look beyond higher education to see how AI affects academic integrity in other educational sectors like K-12, vocational training, and professional certification programs. Each sector may face unique challenges requiring customized solutions for assessment design, policy development, and AI tool integration. Future research can address these issues and help us better understand how to maintain academic integrity in an AI-driven educational landscape while maximizing the benefits of these technologies. Funding: The author received no financial support for the research and/or authorship of this article. Ethics declaration: This study does not require any ethical approval. Declaration of interest: The author declares no competing interest. Data availability: Data generated or analyzed during this study are available from the author on request. 16 / 19 Contemporary Educational Technology, 17(1), ep559 Contemporary Educational Technology, 2025 REFERENCES Ali, D., Fatemi, Y., Boskabadi, E., Nikfar, M., Ugwuoke,

  generalfuture work
  Keywords: educational future technologies tools academic integrity challenges education detection generated ethical author address look beyond
• Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education (2026) · Journal of Information Technology Education Research · cited 1× · doi

  Keywords Conduct multi-institutional replications, experimental interventions on eth- ics/digital literacy training, and studies of assessment design that balance AI use with integrity (e.g., oral/ authentic assessments). artificial intelligence, academic integrity, ChatGPT, Gulf universities, PLS-SEM, student ethical responsibility, transparency, plagiarism avoidance, bias aware- ness, AI trust, digital literacy, AI usefulness, responsible use INTRODUCTION BACKGROUND The rapid integration of artificial intelligence tools such as ChatGPT in the education sector has at- tracted significant scholarly attention (Al-Jahwari & Yousif, 2025; X. Chen et al., 2020; Rejeb et al., 2024; Shishakly, 2025; Vieriu & Petrea, 2025; Zawacki-Richter et al., 2019). From the student per- spective, prior research highlights the benefits of ChatGPT for writing, language learning, research, and administrative tasks (Dwivedi et al., 2023; Fitria, 2023; Lund & Wang, 2023; Shishakly et al., 2025). These systems provide real-time feedback on grammar, programming, and problem-solving by leveraging deep learning techniques to generate contextually relevant responses (Atlas, 2023; Baidoo- Anu & Owusu Ansah, 2023; Else, 2023; Herft, 2023; Kasneci et al., 2023; Qadir, 2022; Sallam, 2023; Sok & Heng, 2023; Susnjak, 2022; Vieriu & Petrea, 2025). Despite these advantages, scholars have raised substantial ethical concerns, including academic dishonesty, over reliance on AI, misinfor- mation, and unfair assessment practices (Rudolph et al., 2023; Sok & Heng, 2023). Although ChatGPT can reduce instructional workload and foster pedagogical innovation (Cox, 2021), it also poses risks to academic integrity, responsible use, and algorithmic fairness (Farhi et al., 2023; Qadir, 2022; Welding, 2023). Ethical AI use is therefore expected to uphold fairness, transparency, privacy, and non-discrimination (Mhlanga, 2023). Plagiarism and contract cheating remain among the most pressing concerns (Cotton et al., 2024; Roe & Perkins, 2022), while transparency in disclosing AI as- sistance is increasingly emphasized as a foundation of academic credibility (Lamb, 2023; C. Lee & Cha, 2025; Tlili et al., 2023). RESEARCH GAP Although existing studies examine AI adoption and traditional academic misconduct, they offer lim- ited empirical insight into how students conceptualize ethical responsibility when using generative AI tools. Most research focuses narrowly on plagiarism or cheating, with minimal attention to broader ethical dimensions such as transparency, responsible use, and algorithmic bias. Consequently, current academic integrity frameworks do not sufficiently account for AI-specific risks or student-level ethi- cal decision-making. Furthermore, theoretical discussions often lack practical, evidence-based strate- gies to guide the ethical use of AI in educational contexts (Guerrero-Dib et al., 2020; Ramdani, 2018; Zawacki-Richter et al., 2019). Kumar et al. (2024

  generalfuture work
  Keywords: academic ethical integrity chatgpt transparency student plagiarism responsible digital literacy assessment artificial intelligence responsibility bias
• Balancing Innovation and Integrity: Navigating the Challenges of Generative AI in Higher Education (2026) · International Journal of Advanced Corporate Learning (iJAC) · doi

  The risks of AI misuse still need to be addressed directly, rather than being dismissed as paranoia among educators and researchers. AI detection tools are one way to mitigate these risks, but they must be part of a broader strategy that includes education, policy development, and assessment reform. However, detection tools alone are insufficient, as they can mistakenly flag legitimate content. Therefore, we recommend that institutions not only invest in detection technologies but also train faculty to recognize the nuances of AI-generated content. Moreover, AI-resistant assessments should focus on real-world applications and critical thinking, areas where AI is less effective at substituting human input [18]. Looking forward, we believe the future of higher education lies in how well we integrate AI into our teaching and research practices while maintaining our commitment to academic integrity. We should not be afraid of AI; instead, we should become proficient in its use and comfortable with allowing it to represent our voice when appropriate. The key is to remain vigilant about where AI assists and where it overreaches, ensuring that our own intellectual contributions stay at the forefront. By doing so, the entire academic community, comprising administrators, instructors, and students, can harness AI’s potential to enhance the educational experience while upholding the values of integrity, trust, and originality that are central to higher education. 9 DECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES IN THE WRITING PROCESS Statement: During the preparation of this work, the author(s), Norman S. St. Clair and Pamela D. McCrau, used ChatGPT 4.0 in order to improve the readability of the document. 60 International Journal of Advanced Corporate Learning (iJAC) iJAC | Vol. 19 No. 1 (2026) Balancing Innovation and Integrity: Navigating the Challenges of Generative AI in Higher Education After using this tool/service, the author(s) reviewed and edited the content as needed, and we take full responsibility for the content of the publication. 10 REFERENCES [1] A. Bandura, “Social cognitive theory of self-regulation,” Organizational Behavior and Human Decision Processes, vol. 50, no. 2, pp. 248–287, 1991. https://doi.org/10.1016/ 0749-5978(91)90022-L [2] D. R. E. Cotton, P. A. Cotton, and J. Shipway, “Chatting and cheating: Ensuring academic integrity in the era of ChatGPT,” Innovations in Education and Teaching International, vol. 61, no. 2, pp. 228–239, 2024. https://doi.org/10.1080/14703297.2023.2190148 [3] S. Joshi, “Comprehensive review of AI hallucinations: Impacts and mitigation strategies for financial and business applications,” International Journal of Computer Applications Technology and Research, vol. 14, no. 6, pp. 38–50, 2025. https://doi.org/10.7753/ IJCATR1406.1

  generalfuture work
  Keywords: education content integrity detection applications higher academic international https risks tools technologies human teaching ensuring
• Adoption of AI-based proctoring platforms: A multi-stakeholder perspective from the education sector stakeholders (2026) · Journal of Technology and Science Education · doi

  The findings of the study offer valuable recommendations to support the responsible and effective implementation of AI-based proctoring systems. The purpose of these recommendations is to make professors, students, and parents responsive similarly, to ensure technology is shared fairly, and to support using digital assessments. Initially, it is essential to prioritize user-centric design principles to ensure accessibility and ease of use for wide range of users (Luo, 2024). AI-proctoring systems should incorporate intuitive interfaces, simplified and streamlined navigation, and clear, concise instructions to cater to both technologically adept users and those with limited digital proficiency (Somavarapu et al., 2024). Educational institutions should organize frequently orientation sessions, open Q&A (Questions and Answers) forums, and accessible documentation can develop user confidence and mitigate resistance. Moreover, the AI-based proctoring platforms should implement robust data privacy and protection protocols. To ensure ethical implementation, it is necessary to address fairness and algorithmic bias. This includes regular audits of AI algorithms, involvement of independent reviewers, and the establishment of redressal systems for users who perceive injustice in monitoring outcomes. Addressing these concerns is vital to maintain the credibility and integrity of assessment processes. At the policy level, adoption of AI-based proctoring systems should align with India’s Digital Personal Data Protection (DPDP) Act, 2023, ensuring lawful data processing, informed consent, purpose limitation, and adequate security safeguards. Institutions should establish internal AI governance frameworks consistent with national data protection regulations to enhance accountability, transparency, and stakeholder trust. To address the parameter on monitoring effectiveness, it is important to increase parental support (Moran et al., 2004). Providing evidence-based outcomes, such as reduced academic dishonesty and enhanced exam integrity. Furthermore, educational policymakers must embed ethical guidelines and accountability measures into the regulatory framework governing AI applications in education. These should cover transparency, data governance, fairness, and user consent, thereby creating a foundation for responsible innovation. Institutions should also establish mechanisms for ongoing feedback and iterative system improvement. This participatory approach, involving all stakeholders in system refinement, can enhance the responsiveness and effectiveness of AI-based solutions. By implementing the above recommendations, educational stakeholders can support a more equitable, trustworthy, and pedagogically aligned integration of AI-proctoring technologies. Kepping students in mind, since privacy concerns significantly influenced behavioural intention of students, institutions should implement transparent data-handling policies, provide clear consent mechanisms, and communicate how AI-based proctoring data are stored, processed, and deleted. For parents, as awareness and perceived usefulness are key factors, institutions should conduct orientation sessions and provide educational materials explaining system accuracy, fairness, and data protection safeguards. Finally, for faculty members training programs should be introduced to enhance trust in system reliability and ethical usage practices. Developers should incorporate clear explainability features into AI systems. For example, proctoring tools such as Proctorio and Respondus should provide instructors with accessible dashboards that clarify: what data are collected, how behavioral flags are generated, the probability thresholds for detecting “suspicious” activity and known limitations or bias risks. Providing interpretable AI outputs would reduce perceived ethical risk and increase trust among faculty.

  generalrecommendations
  Keywords: proctoring based systems institutions support educational protection ethical system recommendations students ensure digital user users
• Self-Organization and Self-Efficacy as Predictors of Cheating Attitudes in Online Exams: A Self-Regulated Learning Perspective (2026) · International Journal of Educational Methodology · doi

  The findings of this study have important implications for educational practice and policy. Addressing cheating requires solutions that target not only actual behaviors but also the temptation to engage in cheating practices, such as modifying exam settings and strengthening monitoring systems (Henderson, Chung, Awdry, Ashford et al., 2023). For instance, instructors can reduce the potential for cheating in online exams by designing exams that emphasize critical thinking rather than rote memory, such as open-book and application-based formats. Misconduct can be further discouraged by using rotated questions, test versions, and time restrictions (Spiegel & Nivette, 2023). Moreover, proctoring strategies should be continuously adapted to address integrity and student experience, emphasizing transparency and ethical use of technology (Maphalaa & Nkosi, 2025). Additionally, institutions should invest in implementing a supportive evaluation strategy that lessens the stress of a single, critical exam by utilizing a variety of assessment methods, including participation, projects, and performance tasks. However, educators and assessment specialists ought to collaborate to develop learning environments that provide students with academic and psychological skills, particularly self-efficacy and self-organization, which give them the confidence and skills they need to succeed with integrity. Instructors can incorporate self-regulation skill training into the classroom through activities, such as using planners, reflection logs, time management, goal setting, and study

  generalrecommendations
  Keywords: cheating self exam instructors exams critical using time integrity assessment skills important implications educational practice
• Reconceptualizing Teaching in The Era of Artificial Intelligence: Evidence from Contemporary Research (2026) · Journal on Innovations in Teaching and Learning · doi

  • Organizations are to introduce organized AI literacy education to enhance the technological and pedagogical skills of teachers. • Educational policymakers must come up with ethical AI governance models to promote transparency, equity, and privacy of data. • Institutions of higher learning and schools ought to encourage interactive human-AI instructional designs other than full automation in terms of teaching. 8. LIMITATIONS • The research involves the primary use of secondary literature and does not involve any primary empirical data. • The high rates of technological changes might restrict the generalizability of the current findings of the AI implementation in the long term 9. CONCLUSION The author concludes that Artificial Intelligence is a revolution and supplementary factor in contemporary learning. Instead of substituting teachers, AI will complement instructional performance by helping to support personal learning, automated testing, data-driven instruction, and interactive learning. The new and dynamic role of teachers as content deliverers will be altered to facilitators, mentors, and strategic decision-makers in the AI-enhanced classroom. Nevertheless, effective AIs implementation needs ethical precautions, institutional preparation, professional training, and moderated administrative systems. Collaborative human-AI pedagogy is the future of AI in education in which technology supplements human knowledge and does not limit teacher autonomy and professional judgment. REFERENCES [1]. Jiménez, AF (2024). Integration of AI helping teachers in traditional teaching roles. European Public &Social Innovation Review, epsir.net, https://epsir.net/index.php/epsir/article/view/664 [2]. Qureshi, I (2025). The impact of AI on teacher roles: Towards a collaborative human-AI pedagogy. AI Edify Journal, researchcorridor.org, https://researchcorridor.org/index.php/aiej/article/view/243 [3]. Taufikin, MSI, Azifah, N, Nikmah, F, & ... (2024). The impact of AI on teacher roles and pedagogy in the 21st century classroom. 2024 International . . . , ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/10617236/ [4]. Zhang, J, & Zhang, Z (2024). AI in teacher education: Unlocking new dimensions in teaching support, inclusive learning, and [5]. digital literacy. Journal of Computer Assisted Learning, Wiley Online Library, https://doi.org/10.1111/jcal.12988 Ivanashko, O, Kozak, A, Knysh, T, & ... (2024). The role of artificial intelligence in shaping the future of education: Opportunities and challenges. Futurity Education, futurity-education.com, https://futurity-education.com/index.php/fed/article/view/262 [6]. Airaj, M (2024). Ethical artificial intelligence for teaching-learning in higher education. Education and Information Technologies, Springer, https://doi.org/10.1007/s10639-024-12545-x [7]. Adhikari, DP, & Pandey, GP (2025). Integrating AI in higher education: transforming teachers’ roles in boosting student agency. Educational Technology Quarterly, acnsci.org, https://acnsci.org/journal/index.php/etq/article/view/943 [8]. Yadav, S (2025). Leveraging AI to enhance teaching and learning in education: The role of artificial intelligence igi-global.com, in modernizing classroom practices. Optimizing research techniques and learning strategies . . . , https://www.igi-global.com/chapter/leveraging-ai-to-enhance-teaching-and-learning-in-education/370742 Joseph, TS, Gowrie, S, Montalbano, MJ, & ... (2025). The roles of artificial intelligence in teaching anatomy: a systematic review. Clinical . . . , Wiley Online Library, https://doi.org/10.1002/ca.24272 [9]. 60 Kiran Soni / Journal on Innovations in Teaching and Learning, Vol 5(1) 2026, 53–61 [10]. Irfan, M, Murray, A case https://researchrepository.ul.ie/entities/publication/12224fd5-78af-45a4-aa3a-ff5acd057025 L, & Ali, critical

  generalrecommendations
  Keywords: education learning https teaching teachers artificial intelligence roles human teacher index article view journal enhance

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